Update README.md

README.md

@@ -20,16 +20,16 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 163041.0
+    num_bytes: 163041
     num_examples: 738
   - name: full_train
-    num_bytes: 951010.0
+    num_bytes: 951010
     num_examples: 4403
   - name: test
-    num_bytes: 384327.0
+    num_bytes: 384327
     num_examples: 1788
   download_size: 718605
-  dataset_size: 1498378.0
+  dataset_size: 1498378
 - config_name: factuality_prediction
   features:
   - name: file
@@ -50,16 +50,16 @@ dataset_info:
     dtype: string
   splits:
   - name: train
-    num_bytes: 606722.0
+    num_bytes: 606722
     num_examples: 2826
   - name: full_train
-    num_bytes: 944929.0
+    num_bytes: 944929
     num_examples: 4403
   - name: test
-    num_bytes: 381863.0
+    num_bytes: 381863
     num_examples: 1788
   download_size: 927856
-  dataset_size: 1933514.0
+  dataset_size: 1933514
 - config_name: original
   features:
   - name: file
@@ -105,4 +105,93 @@ configs:
   data_files:
   - split: train
     path: original/train-*
+license: unknown
+task_categories:
+- text-classification
+language:
+- pt
+- por
+pretty_name: FactNews
+size_categories:
+  - 1K<n<10K
+multilinguality:
+  - monolingual
+language_creators:
+  - found
+annotations_creators:
+  - expert-generated
+tags:
+  - subjectivity
+  - mediabias
+  - media-bias
 ---
+
+## Disclaimer
+
+*I am not the author of this dataset, this is a modified version of the FactCheck dataset on HuggingFace, the original data is made avaliable by Vargas et. al, 2023 and can be downloaded from the link: https://github.com/franciellevargas/FactNews*
+
+*Modifications:*
+- *The "original" subset contains the unmodified original CSV*
+- *The subsets for the task of "bias_prediction" and "factuality_prediction" were splited between train (70%) AND test (30%) by randomly selecting
+  sentences grouped by their id_article. This configuration difers from the authors, who made a 90%/10% 10-fold split on the papers.*
+- *Each task contains an unbalanced split (full-train) and the balanced-split (train)*
+
+# Sentence-Level Annotated Dataset for Predicting Factuality of News and Bias of Media Outlets in Portuguese
+
+Automated fact-checking and news credibility verification at scale require accurate prediction of news factuality and media bias. 
+Here, we introduce a large sentence-level dataset, titled FactNews, composed of 6,191 sentences expertly annotated according to factuality 
+and media bias definitions proposed by AllSides. We used the FactNews to assess the overall reliability of news sources by formulating two 
+text classification problems for predicting sentence-level factuality of news reporting and bias of media outlets. Our experiments demonstrate 
+that biased sentences present a higher number of words compared to factual sentences, besides having a predominance of emotions. Hence, 
+the fine-grained analysis of subjectivity and impartiality of news articles showed promising results for predicting the reliability of the 
+entire media outlet. Finally, due to the severity of fake news and political polarization in Brazil, and the lack of research for Portuguese, 
+both dataset and baseline were proposed for Brazilian Portuguese. The following table describes in detail the FactNews labels, documents, and stories:
+
+| Factual| Quotes | Biased | Total sentences | Total news stories | Total news documents |
+| :---   | :---:  |   ---: |            ---: |               ---: |                  ---: |
+| 4,242  | 1,391  | 558    | 6,161           | 100                | 300                   |
+
+### Sources:
+  - Media 1: Folha de São Paulo
+  - Media 2: Estadão
+  - Media 3: O Globo 
+
+### Paper Results:
+
+Sentence-Level Media Bias Prediction (90%/10% 10-fold split)
+ - 67% (F1-Score) by Fine-tuned mBert-case
+
+Sentence-Level Factuality Prediction (90%/10% 10-fold split)
+- 88% (F1-Score) by Fine-tuned mBert-case
+
+
+## Citation
+
+```
+Vargas, F., Jaidka, K., Pardo, T.A.S., Benevenuto, F. (2023). Predicting Sentence-Level Factuality of News and Bias of Media Outlets. Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pp.1197--1206. Varna, Bulgaria. Association for Computational Linguistics (ACL).
+
+```
+
+**Bibtex**
+```
+@inproceedings{vargas-etal-2023-predicting,
+    title = "Predicting Sentence-Level Factuality of News and Bias of Media Outlets",
+    author = "Vargas, Francielle  and
+      Jaidka, Kokil  and
+      Pardo, Thiago  and
+      Benevenuto, Fabr{\'\i}cio",
+    editor = "Mitkov, Ruslan  and
+      Angelova, Galia",
+    booktitle = "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing",
+    month = sep,
+    year = "2023",
+    address = "Varna, Bulgaria",
+    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
+    url = "https://aclanthology.org/2023.ranlp-1.127",
+    pages = "1197--1206",
+}
+```
+## Dataset Description
+
+- **Homepage:** https://github.com/franciellevargas/FactNews
+- **Paper:** [Predicting Sentence-Level Factuality of News and Bias of Media Outlets](https://aclanthology.org/2023.ranlp-1.127)